Overview

Dataset statistics

Number of variables44
Number of observations100000
Missing cells125341
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.6 MiB
Average record size in memory352.0 B

Variable types

CAT25
BOOL10
NUM8
UNSUPPORTED1

Warnings

contract_start has a high cardinality: 8232 distinct values High cardinality
contract_end has a high cardinality: 365 distinct values High cardinality
date_of_birth has a high cardinality: 14842 distinct values High cardinality
credit_rating has a high cardinality: 77517 distinct values High cardinality
income_salary_per_year has a high cardinality: 37003 distinct values High cardinality
income_deposits_per_year has a high cardinality: 19340 distinct values High cardinality
income_securities_per_year has a high cardinality: 19346 distinct values High cardinality
insurance_life_premium_per_month has a high cardinality: 10809 distinct values High cardinality
insurance_house_premium_per_year has a high cardinality: 45150 distinct values High cardinality
insurance_car_premium_per_year has a high cardinality: 34535 distinct values High cardinality
mortgage_value has a high cardinality: 16677 distinct values High cardinality
mortgage_interest has a high cardinality: 20779 distinct values High cardinality
mortgage_downpayment has a high cardinality: 20795 distinct values High cardinality
customer_limit has a high cardinality: 9833 distinct values High cardinality
cash_withdrawals_value has a high cardinality: 93358 distinct values High cardinality
consumer_credit_value has a high cardinality: 36993 distinct values High cardinality
online_tranactions_per_month is highly correlated with online_number_of_logins_per_monthHigh correlation
online_number_of_logins_per_month is highly correlated with online_tranactions_per_monthHigh correlation
contract_end has 95129 (95.1%) missing values Missing
profession has 7729 (7.7%) missing values Missing
credit_rating has 22483 (22.5%) missing values Missing
credit_rating is uniformly distributed Uniform
last_balance has unique values Unique
last_balance_minus_6_months has unique values Unique
last_balance_minus_12_months has unique values Unique
ZIP is an unsupported type, check if it needs cleaning or further analysis Unsupported
mortgage_dayuntilmaturity has 52065 (52.1%) zeros Zeros
online_number_of_logins_per_month has 29883 (29.9%) zeros Zeros
online_tranactions_per_month has 38723 (38.7%) zeros Zeros
advisor_contacts_last12months has 40313 (40.3%) zeros Zeros
cash_withdraws_per_month has 2996 (3.0%) zeros Zeros
consumer_credit_maturity has 36022 (36.0%) zeros Zeros
number_of_refusals has 20578 (20.6%) zeros Zeros

Reproduction

Analysis started2022-10-19 14:35:00.662119
Analysis finished2022-10-19 14:35:31.736057
Duration31.07 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

contract_start
Categorical

HIGH CARDINALITY

Distinct8232
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2011-10-06
 
44
2010-12-31
 
44
2012-04-20
 
44
2014-04-15
 
42
2012-02-25
 
41
Other values (8227)
99785 
ValueCountFrequency (%) 
2011-10-0644< 0.1%
 
2010-12-3144< 0.1%
 
2012-04-2044< 0.1%
 
2014-04-1542< 0.1%
 
2012-02-2541< 0.1%
 
2010-06-2140< 0.1%
 
2011-09-0739< 0.1%
 
2010-02-2339< 0.1%
 
2012-01-1938< 0.1%
 
2010-09-2838< 0.1%
 
Other values (8222)9959199.6%
 
2022-10-19T16:35:31.876056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1043 ?
Unique (%)1.0%
2022-10-19T16:35:32.010086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

contract_end
Categorical

HIGH CARDINALITY
MISSING

Distinct365
Distinct (%)7.5%
Missing95129
Missing (%)95.1%
Memory size781.2 KiB
2018-06-08
 
23
2017-07-16
 
22
2018-01-02
 
21
2018-01-29
 
21
2018-06-07
 
21
Other values (360)
4763 
ValueCountFrequency (%) 
2018-06-0823< 0.1%
 
2017-07-1622< 0.1%
 
2018-01-0221< 0.1%
 
2018-01-2921< 0.1%
 
2018-06-0721< 0.1%
 
2017-11-1021< 0.1%
 
2018-05-0521< 0.1%
 
2017-12-1120< 0.1%
 
2017-11-0420< 0.1%
 
2017-10-0520< 0.1%
 
Other values (355)46614.7%
 
(Missing)9512995.1%
 
2022-10-19T16:35:32.145083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:32.295089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.34097
Min length3

date_of_birth
Categorical

HIGH CARDINALITY

Distinct14842
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1991-08-01
 
30
1990-09-25
 
28
1993-06-21
 
26
1990-03-09
 
25
1994-11-29
 
25
Other values (14837)
99866 
ValueCountFrequency (%) 
1991-08-0130< 0.1%
 
1990-09-2528< 0.1%
 
1993-06-2126< 0.1%
 
1990-03-0925< 0.1%
 
1994-11-2925< 0.1%
 
1986-07-1724< 0.1%
 
1988-03-0124< 0.1%
 
1990-02-2124< 0.1%
 
1991-05-0824< 0.1%
 
1989-05-3124< 0.1%
 
Other values (14832)9974699.7%
 
2022-10-19T16:35:32.685096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3067 ?
Unique (%)3.1%
2022-10-19T16:35:32.818085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
M
50186 
W
49814 
ValueCountFrequency (%) 
M5018650.2%
 
W4981449.8%
 
2022-10-19T16:35:32.925086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:33.005085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:33.070085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

profession
Categorical

MISSING

Distinct48
Distinct (%)0.1%
Missing7729
Missing (%)7.7%
Memory size781.2 KiB
Journalism
 
3780
Other
 
1963
Food Service
 
1960
Maint
 
1954
Retiree
 
1941
Other values (43)
80673 
ValueCountFrequency (%) 
Journalism37803.8%
 
Other19632.0%
 
Food Service19602.0%
 
Maint19542.0%
 
Retiree19411.9%
 
Insurance19401.9%
 
Design19371.9%
 
Health Care19311.9%
 
Telecommunications19251.9%
 
Retail19191.9%
 
Other values (38)7102171.0%
 
(Missing)77297.7%
 
2022-10-19T16:35:33.212086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:33.365083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length10
Mean length9.81296
Min length3

size_household
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.66585
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size781.2 KiB
2022-10-19T16:35:33.477085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7937884343
Coefficient of variation (CV)0.4765065488
Kurtosis0.3450517787
Mean1.66585
Median Absolute Deviation (MAD)0
Skewness0.9912552117
Sum166585
Variance0.6301000785
MonotocityNot monotonic
2022-10-19T16:35:33.566085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
15125851.3%
 
23329533.3%
 
31318713.2%
 
421292.1%
 
51260.1%
 
65< 0.1%
 
ValueCountFrequency (%) 
15125851.3%
 
23329533.3%
 
31318713.2%
 
421292.1%
 
51260.1%
 
ValueCountFrequency (%) 
65< 0.1%
 
51260.1%
 
421292.1%
 
31318713.2%
 
23329533.3%
 

ZIP
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size781.4 KiB

segment
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
S2
49949 
S3
20023 
S1
19989 
S4
5042 
S5
4997 
ValueCountFrequency (%) 
S24994949.9%
 
S32002320.0%
 
S11998920.0%
 
S450425.0%
 
S549975.0%
 
2022-10-19T16:35:33.678085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:33.758085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:33.846086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

credit_rating
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct77517
Distinct (%)100.0%
Missing22483
Missing (%)22.5%
Memory size781.2 KiB
89,5685176247469
 
1
94,9248049022298
 
1
87,3476116577815
 
1
91,9291835327086
 
1
84,5714854546893
 
1
Other values (77512)
77512 
ValueCountFrequency (%) 
89,56851762474691< 0.1%
 
94,92480490222981< 0.1%
 
87,34761165778151< 0.1%
 
91,92918353270861< 0.1%
 
84,57148545468931< 0.1%
 
87,73491532430121< 0.1%
 
95,97786149083991< 0.1%
 
92,0569942172741< 0.1%
 
89,87895991939911< 0.1%
 
97,74294895813131< 0.1%
 
Other values (77507)7750777.5%
 
(Missing)2248322.5%
 
2022-10-19T16:35:34.118086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique77517 ?
Unique (%)100.0%
2022-10-19T16:35:34.260086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length16
Mean length12.99135
Min length3
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
77517 
1
22483 
ValueCountFrequency (%) 
07751777.5%
 
12248322.5%
 
2022-10-19T16:35:34.339086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
51388 
1
48612 
ValueCountFrequency (%) 
05138851.4%
 
14861248.6%
 
2022-10-19T16:35:34.378096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
72926 
0
27074 
ValueCountFrequency (%) 
17292672.9%
 
02707427.1%
 
2022-10-19T16:35:34.417085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
64018 
1
35982 
ValueCountFrequency (%) 
06401864.0%
 
13598236.0%
 
2022-10-19T16:35:34.454086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
52039 
1
47961 
ValueCountFrequency (%) 
05203952.0%
 
14796148.0%
 
2022-10-19T16:35:34.493086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
77719 
1
22281 
ValueCountFrequency (%) 
07771977.7%
 
12228122.3%
 
2022-10-19T16:35:34.530085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
92375 
2
 
7625
ValueCountFrequency (%) 
19237592.4%
 
276257.6%
 
2022-10-19T16:35:34.594085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:34.657085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:34.724086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
71051 
0
28949 
ValueCountFrequency (%) 
17105171.1%
 
02894928.9%
 
2022-10-19T16:35:34.796086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1
69440 
0
30560 
ValueCountFrequency (%) 
16944069.4%
 
03056030.6%
 
2022-10-19T16:35:34.833085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
57489 
1
42511 
ValueCountFrequency (%) 
05748957.5%
 
14251142.5%
 
2022-10-19T16:35:34.873085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
79307 
1
20693 
ValueCountFrequency (%) 
07930779.3%
 
12069320.7%
 
2022-10-19T16:35:34.911085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

last_balance
Categorical

UNIQUE

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
38694,3768752972
 
1
54312,7512478707
 
1
54560,43741961
 
1
43548,8574706653
 
1
60938,1378131826
 
1
Other values (99995)
99995 
ValueCountFrequency (%) 
38694,37687529721< 0.1%
 
54312,75124787071< 0.1%
 
54560,437419611< 0.1%
 
43548,85747066531< 0.1%
 
60938,13781318261< 0.1%
 
32297,88364800811< 0.1%
 
81187,1243633021< 0.1%
 
37897,76146202441< 0.1%
 
22237,30818879161< 0.1%
 
42081,1055872151< 0.1%
 
Other values (99990)99990> 99.9%
 
2022-10-19T16:35:35.198085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique100000 ?
Unique (%)100.0%
2022-10-19T16:35:35.325087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length16
Mean length15.89452
Min length12
Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
57898,0655263417
 
1
39770,0504600203
 
1
47443,9047808202
 
1
44048,7132229436
 
1
26723,1327812883
 
1
Other values (99995)
99995 
ValueCountFrequency (%) 
57898,06552634171< 0.1%
 
39770,05046002031< 0.1%
 
47443,90478082021< 0.1%
 
44048,71322294361< 0.1%
 
26723,13278128831< 0.1%
 
8512,036525823681< 0.1%
 
70676,90534222011< 0.1%
 
26376,5614898831< 0.1%
 
17684,36311895341< 0.1%
 
39088,41451885751< 0.1%
 
Other values (99990)99990> 99.9%
 
2022-10-19T16:35:35.688093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique100000 ?
Unique (%)100.0%
2022-10-19T16:35:35.848085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length16
Mean length15.89248
Min length11
Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
57993,5440013713
 
1
38077,9706216939
 
1
37027,1427059187
 
1
30389,1710090917
 
1
26870,6693283606
 
1
Other values (99995)
99995 
ValueCountFrequency (%) 
57993,54400137131< 0.1%
 
38077,97062169391< 0.1%
 
37027,14270591871< 0.1%
 
30389,17100909171< 0.1%
 
26870,66932836061< 0.1%
 
16438,79774761131< 0.1%
 
60298,28179484991< 0.1%
 
13600,84294619741< 0.1%
 
13684,61007357341< 0.1%
 
30623,98352190941< 0.1%
 
Other values (99990)99990> 99.9%
 
2022-10-19T16:35:36.381086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique100000 ?
Unique (%)100.0%
2022-10-19T16:35:36.519084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length16
Mean length15.88889
Min length11

income_salary_per_year
Categorical

HIGH CARDINALITY

Distinct37003
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
62998 
75844,2159256729
 
1
130055,175778915
 
1
92443,3103174905
 
1
61751,8858750527
 
1
Other values (36998)
36998 
ValueCountFrequency (%) 
06299863.0%
 
75844,21592567291< 0.1%
 
130055,1757789151< 0.1%
 
92443,31031749051< 0.1%
 
61751,88587505271< 0.1%
 
20071,17600162551< 0.1%
 
51585,22991708581< 0.1%
 
3562,980295042771< 0.1%
 
70723,2941270181< 0.1%
 
4716,048480515721< 0.1%
 
Other values (36993)3699337.0%
 
2022-10-19T16:35:36.744085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique37002 ?
Unique (%)37.0%
2022-10-19T16:35:36.894055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length1
Mean length6.50883
Min length1

income_deposits_per_year
Categorical

HIGH CARDINALITY

Distinct19340
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
80661 
3707,13788215387
 
1
1819,45658405151
 
1
548,831653585611
 
1
2520,83413116782
 
1
Other values (19335)
19335 
ValueCountFrequency (%) 
08066180.7%
 
3707,137882153871< 0.1%
 
1819,456584051511< 0.1%
 
548,8316535856111< 0.1%
 
2520,834131167821< 0.1%
 
3064,961837057141< 0.1%
 
2451,887438104141< 0.1%
 
334,6053704682311< 0.1%
 
1890,21780626241< 0.1%
 
2251,329278171111< 0.1%
 
Other values (19330)1933019.3%
 
2022-10-19T16:35:37.084100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19339 ?
Unique (%)19.3%
2022-10-19T16:35:37.245056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length1
Mean length3.87969
Min length1

income_securities_per_year
Categorical

HIGH CARDINALITY

Distinct19346
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
80655 
3363,69319632479
 
1
4971,74508626439
 
1
777,316391982913
 
1
1934,93377741518
 
1
Other values (19341)
19341 
ValueCountFrequency (%) 
08065580.7%
 
3363,693196324791< 0.1%
 
4971,745086264391< 0.1%
 
777,3163919829131< 0.1%
 
1934,933777415181< 0.1%
 
2777,067420465091< 0.1%
 
2543,472608562221< 0.1%
 
31,79714515334331< 0.1%
 
1348,596407374921< 0.1%
 
2997,40546390561< 0.1%
 
Other values (19336)1933619.3%
 
2022-10-19T16:35:37.449086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19345 ?
Unique (%)19.3%
2022-10-19T16:35:37.613096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length1
Mean length3.8807
Min length1

insurance_life_premium_per_month
Categorical

HIGH CARDINALITY

Distinct10809
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
89192 
165,980808113851
 
1
80,6324203278167
 
1
124,458770367699
 
1
47,3535436661683
 
1
Other values (10804)
10804 
ValueCountFrequency (%) 
08919289.2%
 
165,9808081138511< 0.1%
 
80,63242032781671< 0.1%
 
124,4587703676991< 0.1%
 
47,35354366616831< 0.1%
 
144,6482698921361< 0.1%
 
72,2662068264621< 0.1%
 
166,0586713067431< 0.1%
 
143,9961781555811< 0.1%
 
245,2159390647831< 0.1%
 
Other values (10799)1079910.8%
 
2022-10-19T16:35:37.778085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10808 ?
Unique (%)10.8%
2022-10-19T16:35:37.904087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length1
Mean length2.60882
Min length1

insurance_house_premium_per_year
Categorical

HIGH CARDINALITY

Distinct45150
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
54851 
469,523792432761
 
1
144,601737729076
 
1
478,245553717459
 
1
267,535037999423
 
1
Other values (45145)
45145 
ValueCountFrequency (%) 
05485154.9%
 
469,5237924327611< 0.1%
 
144,6017377290761< 0.1%
 
478,2455537174591< 0.1%
 
267,5350379994231< 0.1%
 
132,5034630947971< 0.1%
 
1028,844974001691< 0.1%
 
392,3014491477641< 0.1%
 
135,0754615905181< 0.1%
 
126,8940372163621< 0.1%
 
Other values (45140)4514045.1%
 
2022-10-19T16:35:38.130070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique45149 ?
Unique (%)45.1%
2022-10-19T16:35:38.274058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length1
Mean length7.72132
Min length1

insurance_car_premium_per_year
Categorical

HIGH CARDINALITY

Distinct34535
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
65466 
352,065705711732
 
1
565,704888180256
 
1
544,979463049147
 
1
471,799537935451
 
1
Other values (34530)
34530 
ValueCountFrequency (%) 
06546665.5%
 
352,0657057117321< 0.1%
 
565,7048881802561< 0.1%
 
544,9794630491471< 0.1%
 
471,7995379354511< 0.1%
 
372,8526179852681< 0.1%
 
367,6810588625351< 0.1%
 
399,7885370780241< 0.1%
 
377,5718705936341< 0.1%
 
271,1068126570421< 0.1%
 
Other values (34525)3452534.5%
 
2022-10-19T16:35:38.482054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique34534 ?
Unique (%)34.5%
2022-10-19T16:35:38.621085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length1
Mean length6.14069
Min length1

mortgage_value
Categorical

HIGH CARDINALITY

Distinct16677
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
83324 
-269987,125895661
 
1
-734033,697525541
 
1
-1216632,30734018
 
1
-1131868,76521123
 
1
Other values (16672)
16672 
ValueCountFrequency (%) 
08332483.3%
 
-269987,1258956611< 0.1%
 
-734033,6975255411< 0.1%
 
-1216632,307340181< 0.1%
 
-1131868,765211231< 0.1%
 
-140385,2161016141< 0.1%
 
-165758,6728965481< 0.1%
 
-1093443,581563971< 0.1%
 
-41376,07922653681< 0.1%
 
-249109,4304727271< 0.1%
 
Other values (16667)1666716.7%
 
2022-10-19T16:35:38.785086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16676 ?
Unique (%)16.7%
2022-10-19T16:35:38.922056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length1
Mean length3.6498
Min length1

mortgage_interest
Categorical

HIGH CARDINALITY

Distinct20779
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
79222 
0,0180055553361837
 
1
0,0304037053386355
 
1
0,0367960149525952
 
1
0,0278647509999185
 
1
Other values (20774)
20774 
ValueCountFrequency (%) 
07922279.2%
 
0,01800555533618371< 0.1%
 
0,03040370533863551< 0.1%
 
0,03679601495259521< 0.1%
 
0,02786475099991851< 0.1%
 
0,05945636705348351< 0.1%
 
0,02173796443509691< 0.1%
 
0,03666772096193731< 0.1%
 
0,02352173046348821< 0.1%
 
0,02811999141103661< 0.1%
 
Other values (20769)2076920.8%
 
2022-10-19T16:35:39.099056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20778 ?
Unique (%)20.8%
2022-10-19T16:35:39.247055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length1
Mean length4.51347
Min length1

mortgage_downpayment
Categorical

HIGH CARDINALITY

Distinct20795
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
79206 
35696,4185603356
 
1
41568,5937779018
 
1
43933,6505734161
 
1
41811,4252480945
 
1
Other values (20790)
20790 
ValueCountFrequency (%) 
07920679.2%
 
35696,41856033561< 0.1%
 
41568,59377790181< 0.1%
 
43933,65057341611< 0.1%
 
41811,42524809451< 0.1%
 
37113,91535162011< 0.1%
 
39440,23531712221< 0.1%
 
44693,26343128291< 0.1%
 
43423,94590421831< 0.1%
 
31454,14176294791< 0.1%
 
Other values (20785)2078520.8%
 
2022-10-19T16:35:39.429054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20794 ?
Unique (%)20.8%
2022-10-19T16:35:39.577085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length1
Mean length4.09697
Min length1

mortgage_dayuntilmaturity
Real number (ℝ≥0)

ZEROS

Distinct8589
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1527.79658
Minimum0
Maximum17912
Zeros52065
Zeros (%)52.1%
Memory size781.2 KiB
2022-10-19T16:35:39.711085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32925
95-th percentile5743.05
Maximum17912
Range17912
Interquartile range (IQR)2925

Descriptive statistics

Standard deviation2170.625105
Coefficient of variation (CV)1.420755311
Kurtosis2.655511833
Mean1527.79658
Median Absolute Deviation (MAD)0
Skewness1.559219016
Sum152779658
Variance4711613.347
MonotocityNot monotonic
2022-10-19T16:35:39.850086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05206552.1%
 
410526< 0.1%
 
383826< 0.1%
 
395125< 0.1%
 
415125< 0.1%
 
384725< 0.1%
 
383924< 0.1%
 
386224< 0.1%
 
415723< 0.1%
 
424723< 0.1%
 
Other values (8579)4771447.7%
 
ValueCountFrequency (%) 
05206552.1%
 
110< 0.1%
 
28< 0.1%
 
36< 0.1%
 
46< 0.1%
 
ValueCountFrequency (%) 
179121< 0.1%
 
178731< 0.1%
 
177351< 0.1%
 
176591< 0.1%
 
176291< 0.1%
 

mortgage_numbers
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
79206 
1
17772 
2
 
2619
3
 
403
ValueCountFrequency (%) 
07920679.2%
 
11777217.8%
 
226192.6%
 
34030.4%
 
2022-10-19T16:35:40.132086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:40.215085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:40.293085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

online_number_of_logins_per_month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.19481
Minimum0
Maximum67
Zeros29883
Zeros (%)29.9%
Memory size781.2 KiB
2022-10-19T16:35:40.407084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q324
95-th percentile34
Maximum67
Range67
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.1128678
Coefficient of variation (CV)0.8533307454
Kurtosis-0.9336892049
Mean14.19481
Median Absolute Deviation (MAD)11
Skewness0.3080366981
Sum1419481
Variance146.7215663
MonotocityNot monotonic
2022-10-19T16:35:40.541056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02988329.9%
 
2129092.9%
 
1828722.9%
 
1928712.9%
 
1728402.8%
 
2028372.8%
 
2227062.7%
 
1627032.7%
 
2326852.7%
 
1525982.6%
 
Other values (52)4509645.1%
 
ValueCountFrequency (%) 
02988329.9%
 
14970.5%
 
26040.6%
 
37470.7%
 
48540.9%
 
ValueCountFrequency (%) 
672< 0.1%
 
621< 0.1%
 
594< 0.1%
 
582< 0.1%
 
573< 0.1%
 

online_tranactions_per_month
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.18393
Minimum0
Maximum59
Zeros38723
Zeros (%)38.7%
Memory size781.2 KiB
2022-10-19T16:35:40.686085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q316
95-th percentile28
Maximum59
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.04345003
Coefficient of variation (CV)1.093589567
Kurtosis-0.1083454262
Mean9.18393
Median Absolute Deviation (MAD)6
Skewness0.8721481179
Sum918393
Variance100.8708885
MonotocityNot monotonic
2022-10-19T16:35:40.826085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03872338.7%
 
1326702.7%
 
1226042.6%
 
1025682.6%
 
925162.5%
 
1524952.5%
 
1124852.5%
 
1424672.5%
 
824582.5%
 
1624412.4%
 
Other values (47)3857338.6%
 
ValueCountFrequency (%) 
03872338.7%
 
115891.6%
 
216731.7%
 
319081.9%
 
420222.0%
 
ValueCountFrequency (%) 
591< 0.1%
 
552< 0.1%
 
541< 0.1%
 
533< 0.1%
 
524< 0.1%
 

advisor_contacts_last12months
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.65447
Minimum0
Maximum6
Zeros40313
Zeros (%)40.3%
Memory size781.2 KiB
2022-10-19T16:35:40.947087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.807432347
Coefficient of variation (CV)1.092453986
Kurtosis-0.4429484139
Mean1.65447
Median Absolute Deviation (MAD)1
Skewness0.8372880885
Sum165447
Variance3.266811687
MonotocityNot monotonic
2022-10-19T16:35:41.034087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
04031340.3%
 
11553615.5%
 
21529715.3%
 
496709.7%
 
395799.6%
 
557305.7%
 
638753.9%
 
ValueCountFrequency (%) 
04031340.3%
 
11553615.5%
 
21529715.3%
 
395799.6%
 
496709.7%
 
ValueCountFrequency (%) 
638753.9%
 
557305.7%
 
496709.7%
 
395799.6%
 
21529715.3%
 

customer_limit
Categorical

HIGH CARDINALITY

Distinct9833
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
1000
52089 
1600
 
1422
1700
 
1419
1650
 
1409
1500
 
1372
Other values (9828)
42289 
ValueCountFrequency (%) 
10005208952.1%
 
160014221.4%
 
170014191.4%
 
165014091.4%
 
150013721.4%
 
175013561.4%
 
155013471.3%
 
145012951.3%
 
180012851.3%
 
185012561.3%
 
Other values (9823)3575035.8%
 
2022-10-19T16:35:41.187056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9748 ?
Unique (%)9.7%
2022-10-19T16:35:41.322083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length4
Mean length5.12639
Min length2

cash_withdraws_per_month
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.03668
Minimum0
Maximum8
Zeros2996
Zeros (%)3.0%
Memory size781.2 KiB
2022-10-19T16:35:41.434096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.716182983
Coefficient of variation (CV)0.4251471464
Kurtosis0.1989862927
Mean4.03668
Median Absolute Deviation (MAD)1
Skewness0.06303198548
Sum403668
Variance2.94528403
MonotocityNot monotonic
2022-10-19T16:35:41.542096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
43937039.4%
 
31590915.9%
 
51010210.1%
 
694089.4%
 
276737.7%
 
766846.7%
 
147854.8%
 
830733.1%
 
029963.0%
 
ValueCountFrequency (%) 
029963.0%
 
147854.8%
 
276737.7%
 
31590915.9%
 
43937039.4%
 
ValueCountFrequency (%) 
830733.1%
 
766846.7%
 
694089.4%
 
51010210.1%
 
43937039.4%
 

cash_withdrawals_value
Categorical

HIGH CARDINALITY

Distinct93358
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
 
6643
459,840939712736
 
1
490,643543076427
 
1
402,762767102293
 
1
250,16860358357
 
1
Other values (93353)
93353 
ValueCountFrequency (%) 
066436.6%
 
459,8409397127361< 0.1%
 
490,6435430764271< 0.1%
 
402,7627671022931< 0.1%
 
250,168603583571< 0.1%
 
238,6528757195391< 0.1%
 
536,9338894866571< 0.1%
 
612,6281326896871< 0.1%
 
387,2280183058581< 0.1%
 
462,2634507183341< 0.1%
 
Other values (93348)9334893.3%
 
2022-10-19T16:35:41.886086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique93357 ?
Unique (%)93.4%
2022-10-19T16:35:42.022086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length16
Mean length14.90019
Min length1

consumer_credit_value
Categorical

HIGH CARDINALITY

Distinct36993
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
63008 
-24011,393551989
 
1
-9336,26875871059
 
1
-17162,129831476
 
1
-13308,0932376708
 
1
Other values (36988)
36988 
ValueCountFrequency (%) 
06300863.0%
 
-24011,3935519891< 0.1%
 
-9336,268758710591< 0.1%
 
-17162,1298314761< 0.1%
 
-13308,09323767081< 0.1%
 
-12638,12930832311< 0.1%
 
-1639,853339241961< 0.1%
 
-5657,934773599471< 0.1%
 
-675,5400564196161< 0.1%
 
-6856,694999604261< 0.1%
 
Other values (36983)3698337.0%
 
2022-10-19T16:35:42.219083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique36992 ?
Unique (%)37.0%
2022-10-19T16:35:42.354100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length1
Mean length6.87763
Min length1

consumer_credit_maturity
Real number (ℝ≥0)

ZEROS

Distinct1970
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean456.42617
Minimum0
Maximum3003
Zeros36022
Zeros (%)36.0%
Memory size781.2 KiB
2022-10-19T16:35:42.482056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median302
Q3858
95-th percentile1318
Maximum3003
Range3003
Interquartile range (IQR)858

Descriptive statistics

Standard deviation485.093374
Coefficient of variation (CV)1.062807976
Kurtosis-0.7824941581
Mean456.42617
Median Absolute Deviation (MAD)302
Skewness0.6797324981
Sum45642617
Variance235315.5815
MonotocityNot monotonic
2022-10-19T16:35:42.616055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03602236.0%
 
1214710.1%
 
1219690.1%
 
1223680.1%
 
1207680.1%
 
1210660.1%
 
1232660.1%
 
1230660.1%
 
213650.1%
 
34650.1%
 
Other values (1960)6337463.4%
 
ValueCountFrequency (%) 
03602236.0%
 
142< 0.1%
 
2610.1%
 
341< 0.1%
 
4520.1%
 
ValueCountFrequency (%) 
30031< 0.1%
 
25841< 0.1%
 
24631< 0.1%
 
24401< 0.1%
 
24311< 0.1%
 

account_fee
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
10
39985 
15
30192 
0
19885 
20
9938 
ValueCountFrequency (%) 
103998540.0%
 
153019230.2%
 
01988519.9%
 
2099389.9%
 
2022-10-19T16:35:42.746086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-19T16:35:42.820085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:42.920055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.80115
Min length1

number_of_refusals
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.04313
Minimum0
Maximum31
Zeros20578
Zeros (%)20.6%
Memory size781.2 KiB
2022-10-19T16:35:43.024056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile13
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.241062328
Coefficient of variation (CV)0.8409583588
Kurtosis-0.1401838673
Mean5.04313
Median Absolute Deviation (MAD)3
Skewness0.6533505077
Sum504313
Variance17.98660967
MonotocityNot monotonic
2022-10-19T16:35:43.128055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
02057820.6%
 
480358.0%
 
579357.9%
 
376997.7%
 
675937.6%
 
271577.2%
 
771247.1%
 
863496.3%
 
162126.2%
 
953495.3%
 
Other values (16)1596916.0%
 
ValueCountFrequency (%) 
02057820.6%
 
162126.2%
 
271577.2%
 
376997.7%
 
480358.0%
 
ValueCountFrequency (%) 
311< 0.1%
 
249< 0.1%
 
2310< 0.1%
 
2219< 0.1%
 
2138< 0.1%
 

Interactions

2022-10-19T16:35:16.057092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.228086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.374086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.517085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.660087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.797086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:16.945085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.098086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.236087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.383085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.543086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.703086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:17.855056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.004097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.280086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.432086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.583094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.728094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:18.883087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.035085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.191097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.336087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.488085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.645086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.794085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:19.933086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.083086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.239087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.386086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.522056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.668085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.822088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:20.967085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.104085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.266058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.443086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.600055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.743086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:21.937095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.095085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.252056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.453055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.620086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.775086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:22.932085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:23.086055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:23.258095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:23.599085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:23.771087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:23.913085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.083096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.262056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.422085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.571077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.732085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:24.923056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.094055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.241055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.433085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.589087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.735085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:25.876084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:26.023056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:26.170085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-19T16:35:43.256056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-19T16:35:43.727054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-19T16:35:44.072085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-19T16:35:44.397057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-10-19T16:35:44.717056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-10-19T16:35:27.043085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:29.393102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:30.627087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-19T16:35:31.122084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sample

First rows

contract_startcontract_enddate_of_birthgenderprofessionsize_householdZIPsegmentcredit_ratingno_credit_rating_flagmain_account_flagonline_banking_flagtele_banking_flagcreditcard_flaginsurance_life_flaginsurance_house_flaginsurance_car_flaginsurance_other_flagmortgage_flagportfolio_flaglast_balancelast_balance_minus_6_monthslast_balance_minus_12_monthsincome_salary_per_yearincome_deposits_per_yearincome_securities_per_yearinsurance_life_premium_per_monthinsurance_house_premium_per_yearinsurance_car_premium_per_yearmortgage_valuemortgage_interestmortgage_downpaymentmortgage_dayuntilmaturitymortgage_numbersonline_number_of_logins_per_monthonline_tranactions_per_monthadvisor_contacts_last12monthscustomer_limitcash_withdraws_per_monthcash_withdrawals_valueconsumer_credit_valueconsumer_credit_maturityaccount_feenumber_of_refusals
02010-10-24NaN1991-02-11MProfessional Services176829S289,56851762474690001001111038694,376875297257898,065526341757993,54400137130000000000000310004459,840939712736-24011,393551989205156
12010-04-302017-10-201986-04-06WNaN135104S294,27050691079660011101101077759,130543659667914,686670349555263,270242991700000000000168210007476,334458774319-5368,74306807524137156
22013-05-02NaN1993-04-18MTransportation163450S293,03094659572830010101101046701,9564859752687,329262682356011,5780893348000000000002612210003332,640202190496-9678,71341935483624154
32016-12-24NaN1994-07-29WProfessional Services29573S3NaN1100101110010917,78497192399506,8208616321812194,65279386670000486,580576010278335,884723643912000104000410504378,32474448561-12110,30591569751263107
42011-10-01NaN1990-10-28WResearch166916S290,457664395710010101100026170,372854333629474,065533762216830,9915503826000000000002318310004471,5092992992790521103
52004-09-24NaN1987-11-18MReal Estate114728S2NaN1010111111028674,653068661226659,817893301826483,530388131000000000003422410003478,17920239483900010
62015-12-20NaN1992-06-30MEngineering295666S2NaN1110101011078017,400928874957371,457302901755035,728144838783863,6390255504000341,5495900047410-318977,4363951970,04173106061320341364,777211402179321910418504588,62147069026101498150
72016-07-15NaN1995-07-20WNaN267819S3NaN1110011101058755,392029110536206,381882274131104,375604524368538,387670900800202,617475598589329,638679298053282,481710455156-299135,8596535820,027971806901714141600,14214908664084161016004635,5469347835890686205
82004-04-17NaN1984-03-11MMaint273113S296,61493938597330110111011041894,491317053556360,038217942440616,516286232565561,646077063600189,759869863685282,0886123896470-179526,7450301960,024065829054001141339,88138076272461138226505484,351735774945-4880,28751322258133205
92015-06-18NaN1984-03-18MConsultant131717S292,023241969094700001111100-3726,831911547856211,5050607880716077,317257972000000000000031000449,5698354249929-19610,275364795807

Last rows

contract_startcontract_enddate_of_birthgenderprofessionsize_householdZIPsegmentcredit_ratingno_credit_rating_flagmain_account_flagonline_banking_flagtele_banking_flagcreditcard_flaginsurance_life_flaginsurance_house_flaginsurance_car_flaginsurance_other_flagmortgage_flagportfolio_flaglast_balancelast_balance_minus_6_monthslast_balance_minus_12_monthsincome_salary_per_yearincome_deposits_per_yearincome_securities_per_yearinsurance_life_premium_per_monthinsurance_house_premium_per_yearinsurance_car_premium_per_yearmortgage_valuemortgage_interestmortgage_downpaymentmortgage_dayuntilmaturitymortgage_numbersonline_number_of_logins_per_monthonline_tranactions_per_monthadvisor_contacts_last12monthscustomer_limitcash_withdraws_per_monthcash_withdrawals_valueconsumer_credit_valueconsumer_credit_maturityaccount_feenumber_of_refusals
999902008-05-21NaN1980-09-26WTelecommunications163864S3NaN1001101111049468,557926065536889,647441960227414,227068742900000000000001100040030102
999912014-03-15NaN1959-06-19WEducation141065S2NaN1101001100050064,98305739241437,552767140326289,718835591611846,3632956555000431,401598717947404,5593297433010004089000514003687,19131918524600209
999922014-02-07NaN1997-07-13MInsurance113627S4NaN1101101110062124,818369488347095,64398592922122,293266107637032,3258030916000576,575396861768273,83066812720900043270004872,0831030247636336,60442623836201104159
999932010-01-30NaN1987-04-03WJournalism194501S594,5484293200810011111111191113,574703988894713,553174762298890,616605132702494,23816797312152,905720216442000000002214310003408,789204989793-4945,243073548461091102
999942006-11-10NaN1961-12-22MHotel482319S592,11881787157290000001100046907,899242538836371,527353055722413,47379954150000000000000310004449,512902229926-5452,1570821956290100
999952006-09-11NaN1985-03-02MFinance259505S1NaN1100001010034127,597013631519034,248573616316966,152848532117700,6113862137000546,6932436892540000149600041550720,1970553916710137300
999962005-04-04NaN1979-09-28WConstruction151143S396,53439380263650000101111027802,953030929923882,084241186929385,81553999220000000000000110004294,554004818315-22473,9139129287810103
999972017-09-05NaN1996-04-25WConstruction16577S285,9821469431760010101111053243,041481201534802,069603916544105,52839901440000000000030110003109,99159757182301063152
999982008-10-31NaN1988-03-23WInformation Technology179115S1NaN1110001110160381,244077230946968,420157539833500,210453571638064,899874081501858,414702428720639,616323588513470,88241886068600017403123317503299,8175866660070004
999992016-10-17NaN1998-03-21WManagement221465S285,72132344590660010101111050544,176973657252957,952793673655139,4492887102000000000001912010004350,62920550338400151